An Analysis of the Potential Applications of Big Data Analytics (BDA) in Supply Chain Management: Emerging Market Perspective

Big Data is defined as the techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions. Big Data can be utilized to gain critical and fundamental insights towards optimizing the supply chain decisions more effective and efficient. In the recent years, therefore, researchers and practitioners have tried to measure the capabilities of Big Data to optimize Supply Chain Management (SCM) efficiency. This research attempts to provide a clear understanding of Big Data applications on Supply Chain Management in emerging markets, especially in Bangladesh, primarily focusing on four key areas: reducing inventory cost, attaining cost leadership, improving customer service and enhancing speed of delivery. To investigate the potential application of Big Data in supply management, a qualitative research has been conducted. Ten in-depth interviews and a case study have been conducted to collect the relevant information from the supply chain experts of the selected firms. Thematic analysis and Hermeneutic iterative methods of analyses have been used. The results indicate that the supply chain of both physical products and services can be benefited from Big Data analytics. The study also revealed that Big Data can be applied in SCM for operational and development purposes including value discovery, value creation and value capture. This study would help the decision makers and practitioners of Supply Chain Management of diverse fields to adopt Big Data to improve the organizations performance and sustainability. Keywords: Big Data analytics, Supply Chain Management, applications, emerging markets

Adsorptive removal of Remazol Red (RR) from textile effluents using jute stick charcoal (JSC)

The research aims to find out the reusability of jute stick charcoal (JSC) to remove Remazol Red (RR) from textile effluents. The JSC was characterized by scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, and Fourier-transform infrared (FTIR) spectroscopy to analyze the morphology, functional groups, and chemical composition, respectively. The batch adsorption method was applied in this study, and it disclosed that dye uptake depends on various factors, namely, pH, contact time, adsorbent dose, and dye concentration. Notably, 93.12% of the dye was removed with the best removal efficiency at a pH of 1, an adsorbent dose of 0.6 g, and an equilibrium time of 120 min, where the adsorption occurred rapidly in the first 20 min. The Langmuir isotherm model successfully defined the adsorption phenomena, yielding an R2 value of 0.995. The kinetic experimental data followed the pseudo-second-order model (R2 = 0.999). The optimum adsorption parameters were implemented for the effluent obtained from a dye bath where a fabric sample (5 g) was dyed with RR, and 62.4% dye was removed. For the scaled application of JSC to a wastewater stream, the raw textile effluent was also treated, which resulted in 52.6% of dye removal. These results show that JSC is a promising adsorbent for treating textile wastewater. HIGHLIGHTS Adsorption of RR dye on JSC and characterization of JSC were studied.; The adsorption process is highly pH-specific (pH = 1).; Nearly 93.12% of the dye removal was observed at optimized conditions.; The experimental findings fit well with pseudo-second-order and Langmuir models.; The optimum conditions have been implemented on both the dyed sample fabric bath (62.4% removal) and actual raw textile effluent (52.6%).